Session: 07-02-01: Nonlinear Dynamics, Control, and Stochastic Mechanics

Paper Number: 112777

112777 - Predictive Control of the KINOVA Gen3 Robotic Manipulator Using a Nonlinear Model 

In contemporary production settings, reliable but efficient pick-and-place robots are frequently used. Automation systems that automate pick-and-place speed up the process of selecting components and relocating them in a variety of sectors that need extensive machine-human collaboration, as well as in situations where sending people to accomplish a goal becomes increasingly risky. The automation and optimization of the pick and place procedures utilizing various path-planning approach thereby support the expansion of application areas. Yet, the design of a controller faces significant difficulties due to the nonlinearities of robotic manipulators and the unpredictable nature of the ambient factors. As a result, in place of the classic model predictive control (MPC), this work presents the nonlinear MPC (NLMPC) as an acceptable control mechanism for real-time optimization and robust stability. In contrast, the NLMPC puts a focus on performance by solving an online finite horizon optimal control problem and applying the first element of the calculated open-loop input trajectory to the system. To acquire the next control input trajectory, the optimization in NLMPC is solved repeatedly with each newly measured state. When input constraints are available, the modeled system tracks reference trajectories to achieve the aim of recognizing and organizing distinct objects. The modified NLMPC method ensures that the robotic arm does not run into obstacles in the workplace or with itself while reaching, gripping, selecting, and depositing the necessary items. The obstacles are represented as spheres, rectangles, and cylinders in order to ensure a precise approximation of the constraint Jacobian in the definition of the nonlinear model predictive control approach. Because of its strength and usability, KINOVA Gen3 is used as a robotic arm for illustration and validation. Hardware-wise, the use of the KINOVA Gen3 has expanded, offering an even stronger effect as a research instrument. Therefore, it is desirable to look at the nonlinear route planning method for the KINOVA Gen3 robotic arm due to its current popularity. The paper, therefore, offers a nonlinear model predictive control method that guarantees object navigation to a desired posture while avoiding obstacles in a constrained workspace. The NLMPC route planning approach is built using MATLAB toolboxes such as the Robotics System Toolbox, Model Predictive Control Toolbox, and Optimization Toolbox. After the NLMPC is successfully developed, a simulation environment is built including a KINOVA Gen3 robotic arm, components that need to be picked up and put, obstacles that need to be overcome, a shelf for sorting parts according to their color, and a trashcan for discarding parts. The 3D components of the simulation environment are created using the computer-aided design tool SOLIDWORKS. The simulation is finally brought to life by combining all the processes into one using a MATLAB Stateflow chart.

Presenting Author: Amanuel Tereda North Carolina A&T State University

Presenting Author Biography: Amanuel Abrdo Tereda is a graduate student in mechanical engineering at North Carolina Agricultural and Technical State University (NCAT). He completed his MSC in 2021 and is currently pursuing his Ph.D. at NCAT. In 2017, he graduated with a BSc in Mechanical Engineering from Addis Ababa University in Ethiopia. His study focuses on developing adaptable and versatile autonomous machines, such as robotic arms, humanoid robots, and drones, with low-cost and durable materials that may provide crucial support in fast-developing countries like his native Ethiopia. To go along with it, he is also interested in developing novel computational analysis, geometric 3D modeling to generate new knowledge, and computational software for application in next-generation design systems, as well as collaborating robotics with quantum computing. Aside from Engineering, he enjoys playing musical instruments such as the piano and guitar. Because of his experience in both Mechanical Engineering and Music, he has a notion of combining the two fields in the same research, such as operating a robotic arm using musical rhythms or musical chord progressions.

Authors:

Amanuel Tereda North Carolina A&T State University
Sun Yi North Carolina A&T State University